The Comparison of Various Correlation Network Models in Studying Mobility Data for the Analysis of Depression Episodes

Rama Krishna Thelagathoti, Hesham H. Ali

2022

Abstract

Depression is a serious mental health disorder affecting millions of people around the world. Traditional diagnostic approaches are subjective including self-reporting feedback from patients and observational evaluation by a trained physician. However, altered motor activity is the central feature for depressive disorder. Moreover, recent studies show that the analysis of motor activity is the best predictor in characterizing psychological disorders including depression. With the advent of wearable devices, an individual’s motor activity can be monitored naturally using body worn sensors and feasible to distinguish depressed persons from healthy individuals. In this manuscript, we hypothesis to apply a methodology that takes advantage of motor activity recorded from wearable devices and process mobility patterns for a given group of subjects. Besides, employed a population analysis approach using correlation networks that evaluates mobility parameters of the population and identify subgroups that exhibit similar motor complexity. We have analyzed the mobility data of the given group by extracting three different sets of features using hour-wise, day-wise, and hybrid mobility data. Also, a comparison study of three models is presented by constructing a correlation graph and finding a cluster of individuals exhibiting similar mobility patterns. We found that mobility data using hour-wise features provides the best results compared to the other two models.

Paper Citation

in Harvard Style

Thelagathoti R. and Ali H. (2022). The Comparison of Various Correlation Network Models in Studying Mobility Data for the Analysis of Depression Episodes. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS; ISBN 978-989-758-552-4, SciTePress, pages 200-207. DOI: 10.5220/0010844500003123

in Bibtex Style

@conference{biosignals22,
author={Rama Krishna Thelagathoti and Hesham H. Ali},
title={The Comparison of Various Correlation Network Models in Studying Mobility Data for the Analysis of Depression Episodes},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS},
year={2022},
pages={200-207},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010844500003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS
TI - The Comparison of Various Correlation Network Models in Studying Mobility Data for the Analysis of Depression Episodes
SN - 978-989-758-552-4
AU - Thelagathoti R.
AU - Ali H.
PY - 2022
SP - 200
EP - 207
DO - 10.5220/0010844500003123
PB - SciTePress